Undersampling in action and at scale: application to the COVID-19 pandemic

Andreas Langousis; Alin Andrei Carsteanu

doi:10.1007/s00477-020-01821-0

Undersampling in action and at scale: application to the COVID-19 pandemic

Andreas Langousis, Alin Andrei Carsteanu

Escuela Superior de Física y Matemáticas (ESFM)

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

It is the purpose of this short communication to analyze the possible caveats in the statistical interpretation of collected data, particularly in the light of decision-making concerning the current COVID-19 coronavirus pandemic. A mitigation of undersampling is proposed, based on re-scaling of statistics that can be considered reliable, such as deaths, and epidemic properties like mortality, that may be considered comparable between countries with similar levels of health care, which would not have reached a saturation level.

Translated title of the contribution	Submuestreo en acción y a escala: aplicación a la pandemia de COVID-19
Original language	English
Pages (from-to)	1281-1283
Number of pages	3
Journal	Stochastic Environmental Research and Risk Assessment
Volume	34
Issue number	8
DOIs	https://doi.org/10.1007/s00477-020-01821-0
State	Published - 1 Aug 2020

Keywords

Asymptomatic carriers
COVID-19 pandemic
Susceptible-infectious-removed (SIR) models
Undersampling

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10.1007/s00477-020-01821-0

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abstract = "It is the purpose of this short communication to analyze the possible caveats in the statistical interpretation of collected data, particularly in the light of decision-making concerning the current COVID-19 coronavirus pandemic. A mitigation of undersampling is proposed, based on re-scaling of statistics that can be considered reliable, such as deaths, and epidemic properties like mortality, that may be considered comparable between countries with similar levels of health care, which would not have reached a saturation level.",

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AU - Langousis, Andreas

AU - Carsteanu, Alin Andrei

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N2 - It is the purpose of this short communication to analyze the possible caveats in the statistical interpretation of collected data, particularly in the light of decision-making concerning the current COVID-19 coronavirus pandemic. A mitigation of undersampling is proposed, based on re-scaling of statistics that can be considered reliable, such as deaths, and epidemic properties like mortality, that may be considered comparable between countries with similar levels of health care, which would not have reached a saturation level.

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KW - Susceptible-infectious-removed (SIR) models

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Undersampling in action and at scale: application to the COVID-19 pandemic

Abstract

Keywords

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